Time Series Classification

Time series classification focuses on automatically assigning labels to sequential data, aiming to improve accuracy and efficiency across diverse applications. Current research emphasizes developing robust and efficient models, including those based on random forests, convolutional neural networks (CNNs), transformers, and state-space models, often incorporating techniques like transfer learning, multi-objective optimization, and self-supervised learning to address challenges such as limited data, high dimensionality, and noise. These advancements are significant for various fields, enabling improved accuracy in applications ranging from healthcare diagnostics and financial forecasting to environmental monitoring and industrial process control.